Advances in Optimization Algorithms for Multidisciplinary Engineering Applications: From Classical Methods to AI-Enhanced Solutions

Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Oliva, Diego (Editor), Valdivia, Arturo (Editor), Mousavirad, Seyed Jalaleddin (Editor), Kalita, Kanak (Editor)
Summary:XI, 806 p. 289 illus., 233 illus. in color.
text
Language:English
Published: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Edition:1st ed. 2025.
Series:Studies in Computational Intelligence, 806
Subjects:
Online Access:https://doi.org/10.1007/978-3-031-78440-8
Format: Electronic Book

MARC

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245 1 0 |a Advances in Optimization Algorithms for Multidisciplinary Engineering Applications: From Classical Methods to AI-Enhanced Solutions  |h [electronic resource] /  |c edited by Diego Oliva, Arturo Valdivia, Seyed Jalaleddin Mousavirad, Kanak Kalita. 
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505 0 |a Metaheuristics theory and applications -- Machine learning -- Engineering applications. 
520 |a This book is an authoritative compilation of the latest advancements in optimization techniques. This book covers a wide array of methods ranging from classical to metaheuristic to AI-enhanced approaches. The chapters are meticulously selected and organized in three sections—metaheuristics, machine learning and engineering applications. This allows for an in-depth exploration of diverse topics ranging from image processing to feature selection to data clustering, to practical applications like energy optimization, smart grids, healthcare diagnostics, etc. Each chapter delves into the specific algorithms and applications as well as provides ample theoretical insights. Accordingly, this book is ideally suited for undergraduate and postgraduate students in fields such as science, engineering and computational mathematics. It is also an invaluable resource for courses on artificial intelligence, computational intelligence, etc. Researchers and professionals in evolutionary computation, artificial intelligence and engineering will find the material especially useful for advancing their work and exploring new frontiers in optimization. 
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